Many studies have been published concerning techniques for automation of early detection of malignant melanoma; few studies have been published for early detection of the most common skin cancer, basal cell carcinoma (“BCC”). Yet, the incidence of melanoma in the USA, an estimated 137,310 cases annually, is outnumbered nearly 20-to-1 by the estimated 2,717,097 annual cases of BCC. The growing costs of medical and surgical treatment associated with BCC are increasing the burden on healthcare systems. Because BCC often appears on the face, surgical procedures carry significant pain and potential for deformity.
Since dermoscopy images provide a fair amount of detail and are often relatively inexpensive to obtain, they provide a viable option for application of new computer vision and machine learning technologies for skin cancer detection. Therefore, automatic skin cancer detection using dermoscopy images has potential to disrupt the current clinical practice of waiting until the cancer is advanced and performing a large excision.
Dermoscopy images of skin lesions, especially of BCC, can be difficult to segment, even for dermatologists. This is due to low contrast, variations in lesion color and size, and significant aberrations and artifacts both inside and outside the lesion. To overcome these obstacles, it may be beneficial to determine a combination of robust and efficient techniques that can achieve relatively accurate segmentation. Segmentation with acceptable lesion capture and tolerance may facilitate relatively accurate feature segmentation which, in turn, may facilitate increased classification accuracy.